The present invention relates to physiological monitoring and, more particularly, noise handling in acoustic physiological monitoring.
Real-time physiological monitoring can be helpful in maintaining the health of people as they go about their daily lives. For example, real-time physiological monitoring can enable prompt discovery of a problem with the respiration of a person who suffers from a chronic pulmonary disease or works in a hazardous environment so that the person can obtain emergency medical treatment. Real-time physiological monitoring can be also used to rapidly detect other types of physiological ailments, such as heart maladies, and can be applied in other contexts, such as senior monitoring and sleep monitoring.
Real-time physiological monitoring often invokes the body sound method, which is sometimes called auscultation. In the body sound method, an acoustic transducer mounted on the body of the person captures and acquires an acoustic signal recording respiration and heart sounds. The sound transducer is typically placed over the suprasternal notch or at the lateral neck near the pharynx because the sounds captured in that region typically have a high signal-to-noise ratio and high sensitivity to variation in flow. Once the acoustic signal has been generated, a respiration sequence may be identified in the acoustic signal and respiration parameter estimates (e.g., respiration rate, inspiration/expiration ratio, etc.) may be calculated. Heart rate estimates may also be calculated from a pulse sequence. Health status information based on respiration parameter estimates and heart rate estimates may then be outputted locally to the monitored person or remotely to a clinician.
One problem commonly encountered in real-time acoustic physiological monitoring is parameter estimation error caused by noise. An acoustic signal that records body sounds can be disrupted by several types of noise, such as long-term, moderate amplitude noise introduced by the surrounding environment, or short-term, high amplitude noise introduced by impulse events such as talking, coughing or sneezing. Regardless of the source, noise can mask the vital signs of interest, resulting in erroneous estimation of physiological parameters and outputting of erroneous health status information. In turn, reliance on erroneous health status information can have serious adverse consequences on the health of the monitored person. For example, such information can lead the person or his or her clinician to improperly diagnose health status and cause the person to undergo treatment that is not medically indicated or forego treatment that is medically indicated.
One known approach to combating noise-induced physiological parameter estimation error tries to remove the noise from the acoustic signal, such as by using a reference microphone to measure environmental noise and attempting to cancel the noise through differentiation. However, this approach adds substantial complexity to the monitoring system and at best only offers a piecemeal solution.
Another known approach, disclosed in Fu et al. application Ser. No. 13/065,816, subjects the acoustic signal to dual path analysis, one path configured to detect long-term moderate amplitude noise and another path configured to detect short-term, high amplitude noise, designates portions of the acoustic signal as noisy based on the combined results of the dual path analysis and excludes the noisy portions when estimating physiological parameters. However, this approach adds meaningfully to the complexity of the monitoring system and tends to be more effective at detecting short-term, high amplitude noise than other types of noise.